Explainable AI for Securing Perception-Layer Sensor Data in IoT Environmental Danger Detection Systems

This paper presents an explainable defense framework against perception-layer and Man-in-the-Middle (MitM) attacks in Internet of Things (IoT)-based environmental hazard warning systems. These systems rely on heterogeneous sensors (gas, light, sound, temperature, and humidity) whose integrity is crucial for reliable environmental alerts. Perception-layer attacks such as spoofing, jamming, and data injection can compromise sensor readings, while MitM attacks threaten communication reliability. The proposed approach integrates incremental Dynamic Time Warping (DTW) for time-series anomaly detection with a tree- based ensemble classifier (XGBoost), in addition to Shapley Additive Explanations (SHAP) for interpretability. A comparative evaluation framework jointly considers detection performance and explanation quality through metrics including pre-registering a Casual Ground Truth based on network protocol localized Precision@ K feature overlap metrics (Q), instead of relying on subjective human-expert or global rank correlations to quantitively evaluate the explanation transparency. Experimental simulations using an authentic EdgeIIoT-2022 dataset under 3-fold forward–chaining cross-validation demonstrated high detection accuracy and moderated explainability scores. The results prove the framework’s ability to detect and explain adversarial behaviors in sensor networks, strengthening trust, transparency, and resilience in safety-critical IoT infrastructures.